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Multi-Task Learning

Multi-task learning aims to learn multiple different tasks simultaneously while maximizing performance on one or all of the tasks.

( Image credit: Cross-stitch Networks for Multi-task Learning )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 601–610 of 3687 papers

Title	Date	Tasks	Status	Hype	Score
A Deep Convolutional Neural Networks Based Multi-Task Ensemble Model for Aspect and Polarity Classification in Persian Reviews	Jan 17, 2022	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	0	5
Bi-Preference Learning Heterogeneous Hypergraph Networks for Session-based Recommendation	Nov 2, 2023	Multi-Task LearningSession-Based Recommendations	CodeCode Available	0	5
Restoring Images with Unknown Degradation Factors by Recurrent Use of a Multi-branch Network	Jul 10, 2019	DeblurringImage Restoration	CodeCode Available	0	5
Joint Acne Image Grading and Counting via Label Distribution Learning	Oct 1, 2019	Acne Severity GradingMedical Image Classification	CodeCode Available	0	5
An All-In-One Convolutional Neural Network for Face Analysis	Nov 3, 2016	Age EstimationAll	CodeCode Available	0	5
Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction	Sep 9, 2022	Emotion-Cause Pair ExtractionMulti-Task Learning	CodeCode Available	0	5
Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning	Sep 21, 2016	DecoderMulti-Task Learning	CodeCode Available	0	5
Joint Learning of Saliency Detection and Weakly Supervised Semantic Segmentation	Sep 9, 2019	Multi-Task LearningSaliency Detection	CodeCode Available	0	5
Knowing Earlier what Right Means to You: A Comprehensive VQA Dataset for Grounding Relative Directions via Multi-Task Learning	Jul 6, 2022	DiagnosticMulti-Task Learning	CodeCode Available	0	5
Learning a Repression Network for Precise Vehicle Search	Aug 8, 2017	AttributeMulti-Task Learning	CodeCode Available	0	5

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All datasets QM9 Cityscapes test NYUv2 OMNIGLOT CelebA UTKFace wireframe dataset

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	PCGrad	∆m%	125.7	—	Unverified
2	CAGrad	∆m%	112.8	—	Unverified
3	IMTL-G	∆m%	77.2	—	Unverified
4	Nash-MTL	∆m%	62	—	Unverified
5	BayesAgg-MTL	∆m%	53.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SwinMTL	mIoU	76.41	—	Unverified
2	Nash-MTL	mIoU	75.41	—	Unverified
3	MultiObjectiveOptimization	mIoU	66.63	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SwinMTL	Mean IoU	58.14	—	Unverified
2	Nash-MTL	Mean IoU	40.13	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Gumbel-Matrix Routing	Average Accuracy	93.52	—	Unverified
2	Mixture-of-Experts	Average Accuracy	92.19	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MGDA-UB	Error	8.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BayesAgg-MTL	delta_m	-2.23	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LETR	FH	83.3	—	Unverified